In-Yong Seo

PRINCIPAL COMPONENTS BASED SUPPORT VECTOR REGRESSION MODEL FOR ON-LINE INSTRUMENT CALIBRATION MONITORING IN NPPS

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor’s operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal componentbased auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.

An On-Line Calibration Monitoring Technique Using Support Vector Regression and Principal Component Analysis

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (AASVR) is proposed for the sensor signal validation of the NPP. This paper describes the design of an AASVR-based sensor validation system for a power generation system. Response surface methodology (RSM) is employed to efficiently determine the optimal values of SVR hyperparameters. The proposed PCSVR model was confirmed with actual plant data of Kori Nuclear Power Plant Unit 3 and compared with the AANN model. The results show that the accuracy and sensitivity of the model were very competitive. Hence, this model can be used to monitor sensor performance.

A Clustering Approach to Wind Power Prediction based on Support Vector Regression

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. It is reported that, compared with physical persistent models, statistical techniques and computational methods are more useful for short-term forecasting of wind power. Among them, support vector regression (SVR) has much attention in the literature. This paper proposes an SVR based wind speed forecasting. To improve the forecasting accuracy, a fuzzy clustering is adopted in the process of SVR modeling. An illustrative example is also given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

A Large Scale Smart-Grid field-test in Jeju

Five smart grid projects was started with large scale at Jeju Island in South Korea from 2009, and smart-grid test-bed was started in Nov. 2010. The five projects are Smart Power Grid (SPG), Smart Renewable (SR), Smart Transportation (ST), Smart Place (SP) and Smart Electric Service (SES). Korea government constructed the smart grid public relations center at Nov. 2010 in Jeju Island and there will be continued the field operation and interface testing among five smart gird projects until May. 2013.

Application of Data Processing Technology on Large Clusters to Distribution Automation System

Quantities of data in the DMS (Distribution management system) or SCADA (Supervisory control and data acquisition) system is enormously large as illustrated by the usage of term flooding of data. This enormous quantity of data is transmitted to the status data or event data of the on-site apparatus in real-time. In addition, if GIS (Geographic information system) and AMR (Automatic meter reading), etc are integrated, the quantity of data to be processed in real-time increases unimaginably. Increase in the quantity of data due to addition of system or increase in the on-site facilities cannot be handled through the currently used Single Thread format of data processing technology. However, if Multi Thread technology that utilizes LF-POOL (Leader Follower -POOL) is applied in processing large quantity of data, large quantity of data can be processed in short period of time and the load on the server can be minimized. In this Study, the actual materialization and functions of LF POOL technology are examined.

A Short-Term Wind Speed Forecasting Through Support Vector Regression Regularized by Particle Swarm Optimization

A sustainability of electricity supply has emerged as a critical issue for low carbon green growth in South Korea. Wind power is the fastest growing source of renewable energy. However, due to its own intermittency and volatility, the power supply generated from wind energy has variability in nature. Hence, accurate forecasting of wind speed and power plays a key role in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. This paper presents a short-term wind speed prediction method based on support vector regression. Moreover, particle swarm optimization is adopted to find an optimum setting of hyper-parameters in support vector regression. An illustration is given by real-world data and the effect of model regularization by particle swarm optimization is discussed as well.

GLRT based fault detection in sensor drift monitoring system

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. This paper presents an on-line sensor drift monitoring technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in sensor signal. Also, principal component-based Auto-Associative support vector regression (AASVR) is proposed for the sensor signal validation of the NPP. Response surface methodology (RSM) is employed to efficiently determine the optimal values of SVR hyperparameters. The proposed model was confirmed with actual plant data of Kori NPP Unit 3. The results show that the accuracy of the model and the fault detection performance of the GLRT are very competitive.

Modeling and Comparison for Auto-association using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR) in Online Monitoring Techniques

An online monitoring based upon sensor system is essential to assure both efficient operation and safety in the power plant. Of great importance is modeling for auto-association (AA) in online monitoring technique. The objective of auto-associative models lies in predicting true values of plant operation parameters from sensor signals transmitted. This paper presents two AA models using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). The presented models are useful, in particular, when there are many parameters to monitor in the power plant. Illustrative examples are given by using a real-world plant dataset. AA performances of SVR and PLSR are finally summarized in terms of accuracy and sensitivity. According to our results, SVR shows much higher accuracy and, however, its sensitivity is relatively degraded.

Signal validation based on PCSVR and EULM

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In the previous study, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. In this paper the error uncertainty limit monitoring (EULM) is integrated with PCSVR for the failure detection. This paper describes the design of an AASVR-based sensor validation system for a power generation system. Response surface methodology (RSM) is employed to efficiently determine the optimal values of SVR hyperparameters. The residuals between the estimated signals and the measured signals are inputted to the EULM to detect whether the sensors are failed or not. The proposed sensor monitoring algorithm was verified through applications to the turbine 1st chamber pressure in pressurized water reactor (PWR).